The allelic heterogeneity of BRCA1, BRCA2, and ATM mutations represents a considerable challenge for developing a diagnostic test to identify quickly,efficiently, and inexpensively all possible mutations in these large genes. While methods for mutation detection such as SSCP or direct sequencing have been successful in the research environment for studying small numbers of samples, the potential need to scale up mutation detection to much larger numbers of DNA samples for clinical purposes will rapidly outstrip the throughput capacities of these more laborious methods. An attractive alternative is the use of "DNA chips", high density oligonucleotide arrays synthesized on a silicon surface. In collaboration with Steve Fodor of Affymetrix, we are investigating the application of this method to BRCA1, BRCA2, and ATM mutation detection. Over 90,000 oligonucleotides were synthesized on DNA chips tailored for each gene to represent all possible nucleotide substitutions and common mutations. Two-color competitive cohybridization experiments were performed on samples containing known mutations as well as an initial blinded scan of 25 samples which found all BRCA1 mutations present. We examined the use of DNA chips to obtain sequence information from homologous genes in closely related species. Orthologs of the human BRCA1 exon 11, all approximately 3.4-kb in length and ranging from 98.2 to 83.5% nucleotide identity, were subjected to hybridization-based and conventional dideoxysequencing analysis. Based upon dideoxysequencing results, retrospective guidelines for identifying high fidelity hybridization-based sequence calls were formulated. Prospective application using these rules yielded base calling with up to 99.9% accuracy over stretches of sequence having approximately 99% identity. Analysis of less highly conserved orthologs could still identify conserved nucleotide tracts and subsets of conserved amino acids as well as provide information for designing primers. DNA-chip based assays can be a valuable new technology for obtaining high-throughput cost-effective sequence information from related genomes.